The present invention relates to techniques for determining the response of biological cells to varying levels of a particular stimulus. More specifically, the invention relates to response curves derived from multivariate phenotypic data extracted from images of biological cells.
Purified substances having a desirable combination bio-active properties are rare and often difficult to identify. Recent advances in traditional organic chemistry and the development of rapid combinatorial chemistry techniques have increased the number of compounds that researchers can test for a specific biological activity (e.g., binding to a target). Unfortunately, the vast majority of “hits” generated by such techniques do not possess the right combination of properties to qualify as therapeutic compounds. When these substances are subjected to low throughput cellular and animal tests to establish their therapeutic usefulness, they are typically found to fail in some regard. Unfortunately, such tests are time consuming and costly, thus limiting the number of substances that can be tested. In a like regard, the few hits that do possess the right combination of properties avoid recognition until after the throughput tests are conducted. With better early evaluation techniques, such promising candidates could be identified earlier in the development process and put on a fast track to the marketplace.
Various early evaluation techniques are under investigation and some have shown promise. In particular cellular phenotyping technologies employing sophisticated image analysis have proven very useful in characterizing therapeutic chemicals. Such technologies are generally described in WO/00/70528 published on Nov. 23, 2000. These techniques attempt to classify compounds based on phenotypic changes that they induce. From these changes, detailed mechanisms of action can be deduced.
Typically, researchers attempting to classify a new compound based on mechanism of action wish to know how that compound compares to other known therapeutics. Compounds that exhibit similar biological functioning in some regards may exhibit similarity in other regards as well. One difficulty in assessing similarity is that compounds often have greatly varying potencies. In other words, while two different compounds may operate by the same or similar mechanism of action, one compound may operate at a much lower concentration than the other compound. It is difficult to make meaningful comparison of two such compounds until the dose scales of these compounds have been adjusted. To this end, researchers often use dose response curves to compare compounds. These curves show the biological effectiveness of particular drugs over multiple concentrations. The effect of the drug at each different concentration provides the “points” for the dose response curves.
Typically, such dose response curves are limited to a single particular biological parameter (e.g., cell count or expression of a protein). The numeric value of such parameter is provided as a function of concentration for each compound of interest. The resulting curves can be compared to identify similar trajectories. Two compounds having similar trajectories might be expected to operate by the same mechanism of action, depending upon which biological parameter is being considered. Unfortunately, there are significant limits to the value of such comparisons. Most importantly, many different parameters may contribute to a mechanism's signature. So a simple dose response curve may fail to shed light on a mechanism.
While image analysis techniques for characterizing phenotypes can provide many different characteristics of a compound, their full potential has not yet been realized. Particularly, it would be useful if such techniques could be applied to obtain meaningful dose response information for compounds or other stimuli under investigation.